Abstract: Systems and methods for delivery aware audience segmentation and subsequent delivery of content are described. Embodiments are configured to obtain activity data for a user, assign the user to a user segment based on the activity data using a machine learning model, generate a reach prediction for the user segment, select a media channel for communicating with the user based on the user segment and the reach prediction, and provide targeted content to the user via the selected media channel. According to some aspects, the machine learning model is trained based on content reach data.

Abstract: The present disclosure describes an electronic device. The electronic device includes a memory and a processor. The processor is configured to provide an utterance to an NLU model, which selects an intent from the list of intents based on the utterance. The processor is also configured to cause a response to the utterance to be provided to a user based on the selected intent. The NLU may be trained by obtaining a dataset comprising one or more words, generating a list of intents based at least in part on the dataset, grouping the list of intents into one or more domains, generating a list of training utterances for each intent in the list of intents, and modifying one or more parameters of the NLU model based on the list of intents, the list of training utterances, and/or the one or more domains.

Abstract: Embodiments described herein provide methods and systems for facilitating actively-learned context modeling. In one embodiment, a subset of data is selected from a training dataset corresponding with an image to be compressed, the subset of data corresponding with a subset of data of pixels of the image. A context model is generated using the selected subset of data. The context model is generally in the form of a decision tree having a set of leaf nodes. Entropy values corresponding with each leaf node of the set of leaf nodes are determined. Each entropy value indicates an extent of diversity of context associated with the corresponding leaf node. Additional data from the training dataset is selected based on the entropy values corresponding with the leaf nodes. The updated subset of data is used to generate an updated context model for use in performing compression of the image.

Abstract: Systems and methods for data analysis are described. Embodiments of the present disclosure data analysis include displaying, via a data analysis interface, a data visualization in a first region of the data analysis interface; and displaying, via the data analysis interface, an analysis thread visualization in a second region of the data analysis interface. The analysis thread visualization depicts an analysis thread graph including a first node corresponding to the data visualization and an edge corresponding to an analysis path between the first node and a second node.

Abstract: Techniques discussed herein generally relate to applying machine-learning techniques to design documents to determine relationships among the different style elements within the document. In one example, hypergraph model is trained on a corpus of hypertext markup language (HTML) documents. The trained model is utilized to identifying one or more candidate style elements for a candidate fragment and/or a candidate fragment. Each of the candidates are scored, and at least a portion of the scored candidates are presented as design options for generating a new document.

Abstract: In various examples, a ranking is generated for a set of computing instances based on predicted metrics associated with computing instances. For example, a prediction model estimates various system performance metrics based on information associated with a workload and configuration information associated with computing instances. The system performance metrics estimated by the prediction model are used to rank the set of computing instances.

Abstract: A method, apparatus, and non-transitory computer readable medium for hypergraph processing are described. Embodiments of the present disclosure obtain, by a hypergraph component, a hypergraph that includes a plurality of nodes and a hyperedge, wherein the hyperedge connects the plurality of nodes; perform, by a hypergraph neural network, a node hypergraph convolution based on the hypergraph to obtain an updated node embedding for a node of the plurality of nodes; and generate, by the hypergraph component, an augmented hypergraph based on the updated node embedding.

Abstract: A method includes extracting an action from a document using a machine learning model. The action is associated with an action parameter. The method further includes extracting a plurality of action events corresponding to the action from the document using the machine learning model. The method further includes generating a record associated with the document based on the extracted action. The method further includes populating the record with the action parameter. The method further includes executing an action event in the plurality of action events using the record.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating generation and presentation of insights. In one implementation, a set of data is used to generate a data visualization. A candidate insight associated with the data visualization is generated, the candidate insight being generated in text form based on a text template and comprising a descriptive insight, a predictive insight, an investigative, or a prescriptive insight. A set of natural language insights is generated, via a machine learning model. The natural language insights represent the candidate insight in a text style that is different from the text template. A natural language insight having the text style corresponding with a desired text style is selected for presenting the candidate insight and, thereafter, the selected natural language insight and data visualization are providing for display via a graphical user interface.

Abstract: A method includes receiving a text to be used for generating an image. The method further includes determining whether the text is a visual text using a machine learning model trained to classify whether an input text is non-visual text or visual text. The method further includes responsive to determining that the text is a visual text, generating the image using a second machine learning model based on the text. The method further includes displaying the image and the text.

Abstract: Various embodiments describe techniques for making inferences from graph-structured data using graph convolutional networks (GCNs). The GCNs use various pre-defined motifs to filter and select adjacent nodes for graph convolution at individual nodes, rather than merely using edge-defined immediate-neighbor adjacency for information integration at each node. In certain embodiments, the graph convolutional networks use attention mechanisms to select a motif from multiple motifs and select a step size for each respective node in a graph, in order to capture information from the most relevant neighborhood of the respective node.

Abstract: Techniques are provided for training graph neural networks with heterophily datasets and generating predictions for such datasets with heterophily. A computing device receives a dataset including a graph data structure and processes the dataset using a graph neural network. The graph neural network defines prior belief vectors respectively corresponding to nodes of the graph data structure, executes a compatibility-guided propagation from the set of prior belief vectors and using a compatibility matrix. The graph neural network predicts predicting a class label for a node of the graph data structure based on the compatibility-guided propagations and a characteristic of at least one node within a neighborhood of the node. The computing device outputs the graph data structure where it is usable by a software tool for modifying an operation of a computing environment.

Abstract: Systems and methods for data analysis are described. Embodiments of the present disclosure data analysis include displaying, via a data analysis interface, a data visualization in a first region of the data analysis interface; and displaying, via the data analysis interface, an analysis thread visualization in a second region of the data analysis interface. The analysis thread visualization depicts an analysis thread graph including a first node corresponding to the data visualization and an edge corresponding to an analysis path between the first node and a second node.

Abstract: Systems and methods for dynamic user profile projection are provided. One or more aspects of the systems and methods includes computing, by a prediction component, a predicted number of lookups for a future time period based on a lookup history of a user profile using a lookup prediction model; comparing, by the prediction component, the predicted number of lookups to a lookup threshold; and transmitting, by a projection component, the user profile to an edge server based on the comparison.

Abstract: A system and methods for providing human-invisible AR markers is described. One aspect of the system and methods includes identifying AR metadata associated with an object in an image; generating AR marker image data based on the AR metadata; generating a first variant of the image by adding the AR marker image data to the image; generating a second variant of the image by subtracting the AR marker image data from the image; and displaying the first variant and the second variant of the image alternately at a display frequency to produce a display of the image, wherein the AR marker image data is invisible to a human vision system in the display of the image.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for building time-decayed line graphs from temporal graph networks for efficiently and accurately generating time-aware recommendations. For example, the time-decayed line graph system creates a line graph of the temporal graph network by deriving interaction nodes from temporal edges (e.g., timed interactions) and connecting interactions that share an endpoint node. Then, the time-decayed line graph system determines the edge weights in the line graph based on differences in time between interactions, with interactions that occur closer together in time being connected with higher weights. Notably, by using this method, the derived time-decayed line graph directly represents topological proximity and temporal proximity. Upon generating the time-decayed line graphs, the system performs downstream predictive modeling such as predicted edge classifications and/or temporal link predictions.

Abstract: In implementations of systems for detection and interpretation of log anomalies, a computing device implements an anomaly system to receive input data describing a two-dimensional representation of log templates and timestamps. The anomaly system processes the input data using a machine learning model trained on training data to detect anomalies in two-dimensional representations of log templates and timestamps. A log anomaly is detected in the two-dimensional representation using the machine learning model based on processing the input data. The anomaly system generates an indication of an interpretation of the log anomaly for display in a user interface based on a log template included in the two-dimensional representation.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a graph neural network to generate data recommendations. The disclosed systems generate a digital graph representation comprising user nodes corresponding to users, data attribute nodes corresponding to data attributes, and edges reflecting historical interactions between the users and the data attributes; Moreover, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. In addition, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation.

Abstract: Systems and methods for distributed machine learning are provided. According to one aspect, a method for distributed machine learning includes obtaining, by an edge device, a static machine learning model from a hub device, computing, by the edge device, an objective function for a dynamic machine learning model based on a relationship between the dynamic machine learning model and the static machine learning model, and updating, by the edge device, the dynamic machine learning model based on the objective function.

Abstract: Techniques for recommending hashtags, including trending hashtags, are disclosed. An example method includes accessing a graph. The graph includes video nodes representing videos, historical hashtag nodes representing historical hashtags, and edges indicating associations among the video nodes and the historical hashtag nodes. A trending hashtag is identified. An edge is added to the graph between a historical hashtag node representing a historical hashtag and a trending hashtag node representing the trending hashtag, based on a semantic similarity between the historical hashtag and the trending hashtag. A new video node representing a new video is added to the video nodes of the graph. A graph neural network (GNN) is applied to the graph, and the GNN predicts a new edge between the trending hashtag node and the new video node. The trending hashtag is recommended for the new video based on prediction of the new edge.